Various businesses rely on machine learning models to process large and complex data sets (“big data”) to provide valuable services to their customers. For example, a social networking service may implement a social networking system to provide users with personalized or targeted services that utilize big data. “Big data” is a broad term referring to the use of predictive methods to extract values from large datasets, which are generally so complex that traditional data processing systems are often inadequate in providing relevant insights. For example, analysis of datasets can find new correlations, trends, patterns, categories, etc. between, e.g., a user and a product or service. Existing data processing systems generally utilize one-to-one mappings of attributes of the user and the product/service in their analysis. However, such methodologies do not allow for predictions of correlations, trends, patterns, or categories that are not explicitly expressed in the one-to-one mappings.
The figures depict various embodiments of this disclosure for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated here may be employed without departing from the principles of embodiments described herein.